RankAggreg, an R package for weighted rank aggregation
By Vasyl Pihur, Susmita Datta, Somnath Datta. BMC Bioinformatics, 2009
332 citations recorded by the source corpus.
BackgroundResearchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.ResultsThe RankAggreg package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.ConclusionThe two examples described in the manuscript clearly show the utility of the RankAggreg package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/54a669346b2295cf59c85f4ec8e36b25550ea370
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Citations in the corpus
- A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, methodology): “The Cross-Entropy Monte Carlo algorithm and the Spearman distance are used for the aggregation [22].”